dc.contributor.author | Belk, Marios | en |
dc.contributor.author | Portugal, D. | en |
dc.contributor.author | Germanakos, Panagiotis | en |
dc.contributor.author | Quintas, J. | en |
dc.contributor.author | Christodoulou, Eleni | en |
dc.contributor.author | Samaras, George S. | en |
dc.contributor.editor | Dicheva D. | en |
dc.contributor.editor | Zhang J. | en |
dc.contributor.editor | Cena F. | en |
dc.contributor.editor | Desmarais M. | en |
dc.creator | Belk, Marios | en |
dc.creator | Portugal, D. | en |
dc.creator | Germanakos, Panagiotis | en |
dc.creator | Quintas, J. | en |
dc.creator | Christodoulou, Eleni | en |
dc.creator | Samaras, George S. | en |
dc.date.accessioned | 2019-11-13T10:38:27Z | |
dc.date.available | 2019-11-13T10:38:27Z | |
dc.date.issued | 2016 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/53636 | |
dc.description.abstract | Stress is an unpleasant condition that entails negative emotions such as fear, worry and nervousness. Motivated by existing research that accompanies stress with physical reactions like increased heart rate, blood volume, pupil dilation and skin conductance, this work builds on the premise that measuring such reactions in real-time could implicitly identify stress of older adults at work while interacting with a system. For this purpose, an inhouse computer mouse was built with embedded sensors for measuring the users' heart rate, skin conductance, skin temperature, and grip force. We have developed a probabilistic classification algorithm that receives as input these physiological measurements, and accordingly identifies emotional stress events. This work contributes to a large body of research in user modeling, aiming to identify when computer users are stressed, and accordingly provide intelligent interventions and personalized solutions to help reduce their frustration and prevent negative health conditions. | en |
dc.publisher | CEUR-WS | en |
dc.source | CEUR Workshop Proceedings | en |
dc.source | 24th ACM Conference on User Modeling, Adaptation and Personalisation, UMAP 2016 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84984614246&partnerID=40&md5=0fb0edc78761d41b621c4e98c4c49f58 | |
dc.subject | Heart | en |
dc.subject | Embedded sensors | en |
dc.subject | Physiology | en |
dc.subject | Computer Mouse | en |
dc.subject | Health condition | en |
dc.subject | Older Adults | en |
dc.subject | Physiological measurement | en |
dc.subject | Physiological Sensors | en |
dc.subject | Probabilistic classification | en |
dc.subject | Skin temperatures | en |
dc.title | A computer mouse for stress identification of older adults at work | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.description.volume | 1618 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: | en |
dc.description.notes | Conference code: 123010</p> | en |
dc.contributor.orcid | Belk, Marios [0000-0001-6200-0178] | |
dc.gnosis.orcid | 0000-0001-6200-0178 | |